def __init__(self, n_layers, in_size, out_size, dropout, use_bi_direction,
activation, **kwargs):
argument.check_unexpected_kwargs(
kwargs,
use_cudnn='use_cudnn argument is not supported anymore. '
'Use chainer.using_config')
argument.assert_kwargs_empty(kwargs)
weights = []
direction = 2 if use_bi_direction else 1
for i in six.moves.range(n_layers):
for di in six.moves.range(direction):
weight = link.Link()
for j in six.moves.range(2):
if i == 0 and j < 1:
w_in = in_size
elif i > 0 and j < 1:
w_in = out_size * direction
else:
w_in = out_size
weight.add_param('w%d' % j, (out_size, w_in))
weight.add_param('b%d' % j, (out_size, ))
getattr(weight, 'w%d' % j).data[...] = numpy.random.normal(
0, numpy.sqrt(1. / w_in), (out_size, w_in))
getattr(weight, 'b%d' % j).data[...] = 0
weights.append(weight)
super(NStepRNNBase, self).__init__(*weights)
self.n_layers = n_layers
self.dropout = dropout
self.activation = activation
self.out_size = out_size
self.direction = direction
self.rnn = rnn.n_step_birnn if use_bi_direction else rnn.n_step_rnn
def __init__(self, n_layers, in_size, out_size, dropout, use_bi_direction,
**kwargs):
argument.check_unexpected_kwargs(
kwargs,
use_cudnn='use_cudnn argument is not supported anymore. '
'Use chainer.using_config')
argument.assert_kwargs_empty(kwargs)
weights = []
direction = 2 if use_bi_direction else 1
for i in six.moves.range(n_layers):
for di in six.moves.range(direction):
weight = link.Link()
with weight.init_scope():
for j in six.moves.range(6):
if i == 0 and j < 3:
w_in = in_size
elif i > 0 and j < 3:
w_in = out_size * direction
else:
w_in = out_size
w = variable.Parameter(GlorotNormal(),
(out_size, w_in))
b = variable.Parameter(0, (out_size, ))
setattr(weight, 'w%d' % j, w)
setattr(weight, 'b%d' % j, b)
weights.append(weight)
super(NStepGRUBase, self).__init__(*weights)
self.n_layers = n_layers
self.dropout = dropout
self.out_size = out_size
self.direction = direction
self.rnn = rnn.n_step_bigru if use_bi_direction else rnn.n_step_gru
def __init__(self, n_layers, in_size, out_size, dropout, use_cudnn,
use_bi_direction):
weights = []
direction = 2 if use_bi_direction else 1
for i in six.moves.range(n_layers):
for di in six.moves.range(direction):
weight = link.Link()
for j in six.moves.range(8):
if i == 0 and j < 4:
w_in = in_size
elif i > 0 and j < 4:
w_in = out_size * direction
else:
w_in = out_size
weight.add_param('w%d' % j, (out_size, w_in))
weight.add_param('b%d' % j, (out_size, ))
getattr(weight, 'w%d' % j).data[...] = numpy.random.normal(
0, numpy.sqrt(1. / w_in), (out_size, w_in))
getattr(weight, 'b%d' % j).data[...] = 0
weights.append(weight)
super(NStepLSTMBase, self).__init__(*weights)
self.n_layers = n_layers
self.dropout = dropout
self.use_cudnn = use_cudnn
self.out_size = out_size
self.direction = direction
self.rnn = rnn.n_step_bilstm if use_bi_direction else rnn.n_step_lstm
def __init__(self,
n_layers,
in_size,
out_size,
dropout,
*,
initialW=None,
initial_bias=None,
**kwargs):
if kwargs:
argument.check_unexpected_kwargs(
kwargs,
use_cudnn='use_cudnn argument is not supported anymore. '
'Use chainer.using_config',
use_bi_direction='use_bi_direction is not supported anymore',
activation='activation is not supported anymore')
argument.assert_kwargs_empty(kwargs)
weights = []
if self.use_bi_direction:
direction = 2
else:
direction = 1
W_initializer = initializers._get_initializer(initialW)
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
for i in six.moves.range(n_layers):
for di in six.moves.range(direction):
weight = link.Link()
with weight.init_scope():
for j in six.moves.range(self.n_weights):
if i == 0 and j < self.n_weights // 2:
w_in = in_size
elif i > 0 and j < self.n_weights // 2:
w_in = out_size * direction
else:
w_in = out_size
w = variable.Parameter(W_initializer, (out_size, w_in))
b = variable.Parameter(bias_initializer, out_size)
setattr(weight, 'w%d' % j, w)
setattr(weight, 'b%d' % j, b)
weights.append(weight)
super(NStepRNNBase, self).__init__(*weights)
self.ws = [[
getattr(layer, 'w%d' % i) for i in six.moves.range(self.n_weights)
] for layer in self]
self.bs = [[
getattr(layer, 'b%d' % i) for i in six.moves.range(self.n_weights)
] for layer in self]
self.n_layers = n_layers
self.dropout = dropout
self.out_size = out_size
self.direction = direction
def __init__(self, n_layers, in_size, out_size, dropout, initialW,
initial_bias, use_bi_direction, **kwargs):
argument.check_unexpected_kwargs(
kwargs,
use_cudnn='use_cudnn argument is not supported anymore. '
'Use chainer.using_config')
argument.assert_kwargs_empty(kwargs)
if initial_bias is None:
initial_bias = initializers.constant.Zero()
initialW = initializers._get_initializer(initialW)
weights = []
direction = 2 if use_bi_direction else 1
for i in six.moves.range(n_layers):
for di in six.moves.range(direction):
weight = link.Link()
with weight.init_scope():
for j in six.moves.range(8):
if i == 0 and j < 4:
w_in = in_size
elif i > 0 and j < 4:
w_in = out_size * direction
else:
w_in = out_size
name_w = 'w{}'.format(j)
name_b = 'b{}'.format(j)
w = variable.Parameter(initialW, (out_size, w_in))
b = variable.Parameter(initial_bias, (out_size, ))
setattr(weight, name_w, w)
setattr(weight, name_b, b)
weights.append(weight)
super(NStepLSTMBase, self).__init__(*weights)
self.n_layers = n_layers
self.dropout = dropout
self.out_size = out_size
self.direction = direction
self.rnn = rnn.n_step_bilstm if use_bi_direction else rnn.n_step_lstm
def __init__(
self, n_layers, in_size, out_size, dropout, use_cudnn=True):
weights = []
for i in six.moves.range(n_layers):
weight = link.Link()
for j in six.moves.range(8):
if i == 0 and j < 4:
w_in = in_size
else:
w_in = out_size
weight.add_param('w%d' % j, (out_size, w_in))
weight.add_param('b%d' % j, (out_size,))
getattr(weight, 'w%d' % j).data[...] = numpy.random.normal(
0, numpy.sqrt(1. / w_in), (out_size, w_in))
getattr(weight, 'b%d' % j).data[...] = 0
weights.append(weight)
super(NStepLSTM, self).__init__(*weights)
self.n_layers = n_layers
self.dropout = dropout
self.use_cudnn = use_cudnn
def __init__(
self,
n_layers,
in_size,
out_size,
dropout,
initialW=None,
initial_bias=None,
**kwargs
):
if kwargs:
argument.check_unexpected_kwargs(
kwargs,
use_cudnn="use_cudnn argument is not supported anymore. "
"Use chainer.using_config",
use_bi_direction="use_bi_direction is not supported anymore",
activation="activation is not supported anymore",
)
argument.assert_kwargs_empty(kwargs)
if initialW is None:
W_initializer = normal.LeCunNormal()
else:
W_initializer = initializers._get_initializer(initialW)
if initial_bias is None:
initial_bias = 0
bias_initializer = initializers._get_initializer(initial_bias)
weights = []
if self.use_bi_direction:
direction = 2
else:
direction = 1
for i in range(n_layers):
for di in range(direction):
weight = link.Link()
with weight.init_scope():
for j in range(self.n_weights):
if i == 0 and j < self.n_weights // 2:
w_in = in_size
elif i > 0 and j < self.n_weights // 2:
w_in = out_size * direction
else:
w_in = out_size
w = variable.Parameter(W_initializer, (out_size, w_in))
b = variable.Parameter(bias_initializer, (out_size,))
setattr(weight, "w%d" % j, w)
setattr(weight, "b%d" % j, b)
weights.append(weight)
super().__init__(*weights)
self.ws = [
[getattr(layer, "w%d" % i) for i in range(self.n_weights)] for layer in self
]
self.bs = [
[getattr(layer, "b%d" % i) for i in range(self.n_weights)] for layer in self
]
self.n_layers = n_layers
self.dropout = dropout
self.out_size = out_size
self.direction = direction
